class_docstr: A :obj:`~gpytorch.variational._VariationalDistribution` that is defined to be a multivariate normal distribution
  with a diagonal covariance matrix. This will not be as flexible/expressive as a
  :obj:`~gpytorch.variational.CholeskyVariationalDistribution`.
  
  :param int num_inducing_points: Size of the variational distribution. This implies that the variational mean
      should be this size, and the variational covariance matrix should have this many rows and columns.
  :param batch_shape: Specifies an optional batch size
      for the variational parameters. This is useful for example when doing additive variational inference.
  :type batch_shape: :obj:`torch.Size`, optional
  :param float mean_init_std: (Default: 1e-3) Standard deviation of gaussian noise to add to the mean initialization.
code: def forward(self):
      # TODO: if we don't multiply self._variational_stddev by a mask of one, Pyro models fail
      # not sure where this bug is occuring (in Pyro or PyTorch)
      # throwing this in as a hotfix for now - we should investigate later
      mask = torch.ones_like(self._variational_stddev)
      variational_covar = DiagLinearOperator(
          self._variational_stddev.mul(mask).pow(2)
      )
      return MultivariateNormal(
          self.variational_mean, variational_covar
      )
  
file_path: gpytorch/gpytorch/variational/mean_field_variational_distribution.py
imports: import torch
  from linear_operator.operators import DiagLinearOperator
  from ..distributions import MultivariateNormal
  from ._variational_distribution import _VariationalDistribution
label_desc: Use image node_id 3 for calling the MeanFieldVariationalDistribution obj's underlying member method code with example usage: obj.forward() and returns: MultivariateNormal